Feature Extraction
Transformers
Safetensors
PyTorch
liquid_codec
audio
audio-codec
bfloat16
custom_code
Instructions to use LiquidAI/Liquid-Codec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiquidAI/Liquid-Codec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LiquidAI/Liquid-Codec", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LiquidAI/Liquid-Codec", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from LiquidAI/Liquid-Codec: direct link, hf CLI and curl.
- Browser
- Download file 147 MB
-
https://huggingface.co/LiquidAI/Liquid-Codec/resolve/main/model.safetensors
- Command line
-
hf download hf://LiquidAI/Liquid-Codec/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/LiquidAI/Liquid-Codec/resolve/main/model.safetensors
147 MB
- Xet hash:
- c6cad46f40fb7d38172b08e663c5dc20a3ac845a55b2731d346227712e629b15
- Size of remote file:
- 147 MB
- SHA256:
- 3fa4d7d9b2aef1d12a4a5be269e9d740d5bd09f471d64200b4e073f055a6cd1f
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